The RS Generalized Lambda Distribution Based Calibration Model

Steve Su, Abeer Hasan, Wei Ning

Abstract

We propose a flexible linear calibration model with errors from RS (Ramberg \& Schmeiser, 1974) generalized lambda distribution ($G\lambda D$). We demonstrate the derivation of the maximum likelihood estimates of RS $G\lambda D$ parameters and examine the estimation performance using a simulation study for sample sizes ranging from 30 to 200. The use of RS $G\lambda D$ calibration model not only provides statistical modeller with a richer range of distributional shapes, but can also provide more precise parameter estimates compared to the standard Normal calibration model or skewed Normal calibration model proposed by Figueiredoa, Bolfarinea, Sandovala and Limab (2010).

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